Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions.

Quantitative photoacoustic tomography (QPAT) is a valuable tool in characterizing ovarian lesions for accurate diagnosis. However, accurately reconstructing a lesion's optical absorption distributions from photoacoustic signals measured with multiple wavelengths is challenging because it involves an ill-posed inverse problem with three unknowns: the Grüneisen parameter ( Γ ) , the absorption distribution, and the optical fluence ( ϕ ) . Here, we propose a novel ultrasound-enhanced Unet model (US-Unet) that reconstructs optical absorption distribution from PAT data. A pre-trained ResNet-18 extracts the US features typically identified as morphologies of suspicious ovarian lesions, and a Unet is implemented to reconstruct optical absorption coefficient maps, using the initial pressure and US features extracted by ResNet-18. To test this US-Unet model, we calculated the blood oxygenation saturation values and total hemoglobin concentrations from 655 regions of interest (ROIs) (421 benign, 200 malignant, and 34 borderline ROIs) obtained from clinical images of 35 patients with ovarian/adnexal lesions. A logistic regression model was used to compute the ROC, the area under the ROC curve (AUC) was 0.94, and the accuracy was 0.89. To the best of our knowledge, this is the first study to reconstruct quantitative PAT with PA signals and US-based structural features.

Rectal Cancer Treatment Management: Deep-Learning Neural Network Based on Photoacoustic Microscopy Image Outperforms Histogram-Feature-Based Classification.

We have developed a novel photoacoustic microscopy/ultrasound (PAM/US) endoscope to image post-treatment rectal cancer for surgical management of residual tumor after radiation and chemotherapy. Paired with a deep-learning convolutional neural network (CNN), the PAM images accurately differentiated pathological complete responders (pCR) from incomplete responders. However, the role of CNNs compared with traditional histogram-feature based classifiers needs further exploration. In this work, we compare the performance of the CNN models to generalized linear models (GLM) across 24 ex vivo specimens and 10 in vivo patient examinations. First order statistical features were extracted from histograms of PAM and US images to train, validate and test GLM models, while PAM and US images were directly used to train, validate, and test CNN models. The PAM-CNN model performed superiorly with an AUC of 0.96 (95% CI: 0.95-0.98) compared to the best PAM-GLM model using kurtosis with an AUC of 0.82 (95% CI: 0.82-0.83). We also found that both CNN and GLMs derived from photoacoustic data outperformed those utilizing ultrasound alone. We conclude that deep-learning neural networks paired with photoacoustic images is the optimal analysis framework for determining presence of residual cancer in the treated human rectum.

Fiber endface photoacoustic generator for quantitative photoacoustic tomography.

We demonstrate a novel fiber endface photoacoustic (PA) generator using infrared (IR) 144 laser dye dispersed within an ultraviolet adhesive. The generator provides a wide acoustic bandwidth in the transducer frequency range of 2-7 MHz, high thermal conversion efficiency (${\gt}90\%$), good PA signal controllability (well-controlled IR 144 concentration), and high feasibility (simple procedures). Through a series of experimental validations, we show that this fiber-based endface PA generator can be a useful tool for a broad range of biomedical applications such as calibrating the local absorption coefficient of biological tissue for quantitative PA tomography.

Photoacoustic tomography reconstruction using lag-based delay multiply and sum with a coherence factor improves in vivo ovarian cancer diagnosis.

Ovarian cancer is the fifth most common cause of death due to cancer, and it is the deadliest of all gynecological cancers. Diagnosing ovarian cancer via conventional photoacoustic delay-and-sum beamforming (DAS) presents several challenges, such as poor image resolution and low lesion to background tissue contrast. To address these concerns, we propose an improved beamformer named lag-based delay multiply and sum combined with coherence factor (DMAS-LAG-CF). Simulations and phantom experiments demonstrate that compared with the conventional DAS, the proposed algorithm can provide 1.39 times better resolution and 10.77 dB higher contrast. For patient data, similar performance on contrast ratios has been observed. However, since the diagnostic accuracy between cancer and benign/normal groups is a significant measure, we have extracted photoacoustic histogram features of mean, kurtosis and skewness. DMAS-LAG-CF can improve cancer diagnosis with an AUC of 0.91 for distinguishing malignant vs. benign ovarian lesions when mean and skewness are used as features.

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system comprising an endorectal coregistered photoacoustic (PA) microscopy (PAM) and US system paired with a convolution neural network (CNN) to assess the rectal cancer treatment response. Materials and Methods In this prospective study (ClinicalTrials.gov identifier NCT04339374), participants completed radiation and chemotherapy from September 2019 to September 2020 and images were obtained with the PAM/US system prior to surgery. Another group's colorectal specimens were studied ex vivo. The PAM/US system consisted of an endorectal imaging probe, a 1064-nm laser, and one US ring transducer. The PAM CNN and US CNN models were trained and validated to distinguish normal from malignant colorectal tissue using ex vivo and in vivo patient data. The PAM CNN and US CNN were then tested using additional in vivo patient data that had not been seen by the CNNs during training and validation. Results Twenty-two patients' ex vivo specimens and five patients' in vivo images (a total of 2693 US regions of interest [ROIs] and 2208 PA ROIs) were used for CNN training and validation. Data from five additional patients were used for testing. A total of 32 participants (mean age, 60 years; range, 35-89 years) were evaluated. Unique PAM imaging markers of the complete tumor response were found, specifically including recovery of normal submucosal vascular architecture within the treated tumor bed. The PAM CNN model captured this recovery process and correctly differentiated these changes from the residual tumor. The imaging system remained highly capable of differentiating tumor from normal tissue, achieving an area under the receiver operating characteristic curve of 0.98 (95% CI: 0.98, 0.99) for data from five participants. By comparison, the US CNN had an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.70, 0.73). Conclusion An endorectal coregistered photoacoustic microscopy/US system paired with a convolutional neural network model showed high diagnostic performance in assessing the rectal cancer treatment response and demonstrated potential for optimizing posttreatment management. © RSNA, 2021 Supplemental material is available for this article. See also the editorial by Klibanov in this issue.

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi-spectral photoacoustic tomography.

In photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2 ). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi-pixel method to smooth the measurements before calculating rHbT and %sO2 . We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co-registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90-0.96) on the testing data set was achieved by combining mean %sO2 , a spectral feature, and the score of the study radiologist.

Co-registered photoacoustic and ultrasound imaging of human colorectal cancer.

Colorectal cancer is the second most common malignancy diagnosed globally. Critical gaps exist in diagnostic and surveillance imaging modalities for colorectal neoplasia. Although prior studies have demonstrated the capability of photoacoustic imaging techniques to differentiate normal from neoplastic tissue in the gastrointestinal tract, evaluation of deep tissue with a fast speed and a large field of view remains limited. To investigate the ability of photoacoustic technology to image deeper tissue, we conducted a pilot study using a real-time co-registered photoacoustic tomography (PAT) and ultrasound (US) system. A total of 23 ex vivo human colorectal tissue samples were imaged immediately after surgical resection. Co-registered photoacoustic images of malignancies showed significantly increased PAT signal compared to normal regions of the same sample. The quantitative relative total hemoglobin (rHbT) concentration computed from four optical wavelengths, the spectral features, such as the mean spectral slope, and 0.5-MHz intercept extracted from PAT and US spectral data, and image features, such as the first- and second-order statistics along with the standard deviation of the mean radon transform of PAT images, have shown statistical significance between untreated colorectal tumors and the normal tissue. Using either a logistic regression model or a support vector machine, the best set of parameters of rHbT and PAT intercept has achieved area-under-the-curve (AUC) values of 0.97 and 0.95 for both training and testing data sets, respectively, for prediction of histologically confirmed invasive carcinoma.

Classification of human ovarian cancer using functional, spectral, and imaging features obtained from in vivo photoacoustic imaging.

We report in this pilot study the diagnostic results of in vivo imaging of patients with ovarian lesions, using a co-registered photoacoustic and ultrasound (PAT/US) system. A total of 39 ovaries from 24 patients were imaged in vivo. PAT functional features, i.e., blood oxygen saturation (sO2) and relative total hemoglobin (rHbT), PAT image features, and PAT spectral features within a region of interest (ROI) in each ovarian tissue were extracted. To select the significant features, a t-test on each feature was performed, and the independent predictors were determined by evaluating correlation between each pair of predictors. To classify the ovarian lesions, we employed a generalized linear model (GLM) and a support vector machine (SVM). We used these classifiers first to distinguish benign/normal lesions from ovaries with invasive epithelial tumors and then to separate normal/benign lesions from all types of ovarian tumors. We developed classifiers once by inclusion of PAT functional features to assess the best diagnostic performance of the classifiers when multiple wavelengths data are available. Second time, we excluded the PAT functional features from the features set to evaluate the best diagnostic performance if only a single wavelength is available. Our results show that using functional features improves the classification performance, especially for distinguishing normal/benign ovarian lesions from all types of tumors. In this case, an area under ROC curve (AUC) of 0.92, 0.93 of testing data was achieved using a GLM and SVM classifier when functional features were included in the feature set while excluding these features resulted in an AUC of 0.89, 0.92, respectively.

Optimized light delivery probe using ball lenses for co-registered photoacoustic and ultrasound endo-cavity subsurface imaging.

An optimized hand-held photoacoustic and ultrasound probe suitable for endo-cavity tumor subsurface imaging was designed and evaluated. Compared to previous designs, the prototype probe, consisting of four 1 mm multi-mode optical fibers attached with 1.5 mm diameter ball-shaped fiber tips sandwiched between a transvaginal ultrasound transducer and a custom-made sheath, demonstrated a higher light output and better beam homogeneity on tissue subsurface. The output power and fluence profile were simulated for different design parameters. A camera recorded fluence profiles through calibrated intralipid solution at various imaging depths. The light delivery efficiency was experimentally compared with and without the ball lenses, based on ex-vivo imaging of human colorectal cancer and in-vivo imaging of a palmar vein proximal to the human wrist. The simulations and experiments demonstrated that ball-shaped fiber tip design can achieve homogeneous fluence distribution on tissue subsurface with acceptable light output efficiency, suggesting its clinical potential for in-vivo endo-cavity imaging.

Evaluation of Ovarian Cancer: Initial Application of Coregistered Photoacoustic Tomography and US.

Purpose To assess transvaginal coregistered photoacoustic tomography (PAT) and pulse-echo US for diagnosis of ovarian cancer based on functional parameters provided by PAT. Materials and Methods Between February 2017 and December 2017, 26 ovarian masses from 16 participants were successfully imaged in vivo by multispectral photoacoustic imaging, including nine invasive epithelial ovarian cancers (six serous carcinomas and three endometroid adenocarcinomas), three other tumors (two borderline serous tumors and one sex cord-stromal tumor), and 14 benign and normal (hereafter referred to as benign/normal) ovaries. The relative total hemoglobin concentration (rHbT) and mean oxygen saturation (sO2) shown at PAT were used to characterize the ovaries identified at US. Results The average rHbT was 1.9 times higher for invasive epithelial cancers than for the benign/normal ovaries (P = .01). Additionally, the rHbT distribution was extensive in invasive epithelial cancers, but was scattered in benign/normal ovaries. However, the rHbT of two borderline serous tumors and one stromal tumor was in the same range as that of benign/normal ovaries. The mean sO2 of invasive epithelial cancers, and of the borderline and stromal tumors, was 8.2% lower than that of benign/normal ovaries (P = .003). Discussion Invasive epithelial ovarian cancers showed higher and extensive tumor vascularity and lower oxygen saturation than benign and normal ovaries. Two borderline noninvasive serous and one stromal tumor showed low oxygen saturation compared with benign and normal ovaries. ©RSNA, 2018 Online supplemental material is available for this article.